Identifying security-related requirements in regulatory documents based on cross-project classification

Security is getting substantial focus in many industries, especially safety-critical ones. When new regulations and standards which can run to hundreds of pages are introduced, it is necessary to identify the requirements in those documents which have an impact on security. Additionally, it is necessary to revisit the requirements of existing systems and identify the security related ones. We investigate the feasibility of using a classifier for security-related requirements trained on requirement specifications available online. We base our investigation on 15 requirement documents, randomly selected and partially pre-labelled, with a total of 3,880 requirements. To validate the model, we run a cross-project prediction on the data where each specification constitutes a group. We also test the model on three different United Nations (UN) regulations from the automotive domain with different magnitudes of security relevance. Our results indicate the feasibility of training a model from a heterogeneous data set including specifications from multiple domains and in different styles. Additionally, we show the ability of such a classifier to identify security requirements in real-life regulations and discuss scenarios in which such a classification becomes useful to practitioners

A review of occupational regulation and its impact

This Evidence Report develops a deeper understanding of the nature and impact of occupational regulation in the UK. The term, occupational regulation, is a broad heading for various mechanisms (including licence to practice and voluntary forms) through which minimum skill standards are applied within occupations. As such, occupational regulation is one of a range of levers, or best market solutions, which are designed to encourage employers to train on a collective basis. The use of occupational regulation as a mechanism for increasing the demand for, and supply of, skills was considered alongside other measures, as part of the UK Commission for Employment and Skills’ Review of Employer Collective Measures. However, that Review acknowledged the general topic of occupational regulation was under researched in the UK. This research, conducted by the National Institute of Economic and Social Research, helps to address this and deepens our knowledge of the area by: providing a discussion on the existing theory on occupational regulation by examining existing economic literature; providing a detailed review of the existing evidence on occupational regulation in the UK, America, Canada and Europe (Germany, France and Italy), again via existing literature; providing a comprehensive map of occupational regulation in the UK, through the mapping of managerial, professional and non-professional occupations at the Unit Group level of the Standard Occupational Classification (2000); producing estimates of the labour market impact of occupational regulation in the UK. Its prevalence is estimated by comparing the mapping output with Unit Group data obtained from the Quarterly Labour Force Survey (QLFS). Further analysis, via cross-sectional analysis, produces estimates on levels of qualifications, wages and rates of job-related training between workers in regulated and unregulated occupations. This uses QLFS 2010 data. And a Difference-in-Differences analysis is employed to evaluate the impact of switches in regulation status on skill levels, job-related education and training, wages and employment. This uses QLFS data between 2001 and 2010

Grid infrastructures for secure access to and use of bioinformatics data: experiences from the BRIDGES project

The BRIDGES project was funded by the UK Department of Trade and Industry (DTI) to address the needs of cardiovascular research scientists investigating the genetic causes of hypertension as part of the Wellcome Trust funded (£4.34M) cardiovascular functional genomics (CFG) project. Security was at the heart of the BRIDGES project and an advanced data and compute grid infrastructure incorporating latest grid authorisation technologies was developed and delivered to the scientists. We outline these grid infrastructures and describe the perceived security requirements at the project start including data classifications and how these evolved throughout the lifetime of the project. The uptake and adoption of the project results are also presented along with the challenges that must be overcome to support the secure exchange of life science data sets. We also present how we will use the BRIDGES experiences in future projects at the National e-Science Centre

What Works Better? A Study of Classifying Requirements

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.Comment: 7 pages, the 25th IEEE International Conference on Requirements Engineering (RE'17

Data curation standards and social science occupational information resources

Occupational information resources - data about the characteristics of different occupational positions - are widely used in the social sciences, across a range of disciplines and international contexts. They are available in many formats, most often constituting small electronic files that are made freely downloadable from academic web-pages. However there are several challenges associated with how occupational information resources are distributed to, and exploited by, social researchers. In this paper we describe features of occupational information resources, and indicate the role digital curation can play in exploiting them. We report upon the strategies used in the GEODE research project (Grid Enabled Occupational Data Environment, http://www.geode.stir.ac.uk). This project attempts to develop long-term standards for the distribution of occupational information resources, by providing a standardized framework-based electronic depository for occupational information resources, and by providing a data indexing service, based on e-Science middleware, which collates occupational information resources and makes them readily accessible to non-specialist social scientists

Supporting the clinical trial recruitment process through the grid

Patient recruitment for clinical trials and studies is a large-scale task. To test a given drug for example, it is desirable that as large a pool of suitable candidates is used as possible to support reliable assessment of often moderate effects of the drugs. To make such a recruitment campaign successful, it is necessary to efficiently target the petitioning of these potential subjects. Because of the necessarily large numbers involved in such campaigns, this is a problem that naturally lends itself to the paradigm of Grid technology. However the accumulation and linkage of data sets across clinical domain boundaries poses challenges due to the sensitivity of the data involved that are atypical of other Grid domains. This includes handling the privacy and integrity of data, and importantly the process by which data can be collected and used, and ensuring for example that patient involvement and consent is dealt with appropriately throughout the clinical trials process. This paper describes a Grid infrastructure developed as part of the MRC funded VOTES project (Virtual Organisations for Trials and Epidemiological Studies) at the National e-Science Centre in Glasgow that supports these processes and the different security requirements specific to this domain